Overview

Dataset statistics

Number of variables22
Number of observations32950
Missing cells21140
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 MiB
Average record size in memory713.3 B

Variable types

Numeric12
Categorical10

Alerts

cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 4 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 1 other fieldsHigh correlation
pastEmail is highly overall correlated with previousHigh correlation
pdays is highly overall correlated with pmonths and 2 other fieldsHigh correlation
pmonths is highly overall correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 1 other fieldsHigh correlation
previous is highly overall correlated with pastEmail and 2 other fieldsHigh correlation
default is highly imbalanced (52.9%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (57.1%)Imbalance
custAge has 8042 (24.4%) missing valuesMissing
schooling has 9770 (29.7%) missing valuesMissing
day_of_week has 3328 (10.1%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
previous has 28503 (86.5%) zerosZeros
pastEmail has 29120 (88.4%) zerosZeros

Reproduction

Analysis started2024-09-05 16:01:53.096747
Analysis finished2024-09-05 16:03:14.172401
Duration1 minute and 21.08 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

custAge
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)0.3%
Missing8042
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean40.035852
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:14.567867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.425448
Coefficient of variation (CV)0.2604028
Kurtosis0.72729411
Mean40.035852
Median Absolute Deviation (MAD)7
Skewness0.76325998
Sum997213
Variance108.68997
MonotonicityNot monotonic
2024-09-05T21:33:15.184234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1152
 
3.5%
32 1137
 
3.5%
33 1100
 
3.3%
36 1081
 
3.3%
35 1041
 
3.2%
34 1040
 
3.2%
30 1014
 
3.1%
37 891
 
2.7%
29 880
 
2.7%
39 865
 
2.6%
Other values (65) 14707
44.6%
(Missing) 8042
24.4%
ValueCountFrequency (%)
17 4
 
< 0.1%
18 18
 
0.1%
19 26
 
0.1%
20 44
 
0.1%
21 66
 
0.2%
22 74
 
0.2%
23 143
 
0.4%
24 293
0.9%
25 374
1.1%
26 432
1.3%
ValueCountFrequency (%)
98 2
 
< 0.1%
92 2
 
< 0.1%
91 1
 
< 0.1%
89 1
 
< 0.1%
88 15
< 0.1%
86 7
< 0.1%
85 7
< 0.1%
84 4
 
< 0.1%
83 7
< 0.1%
82 9
< 0.1%

profession
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
admin.
8320 
blue-collar
7407 
technician
5392 
services
3177 
management
2341 
Other values (7)
6313 

Length

Max length13
Median length12
Mean length8.9548407
Min length6

Characters and Unicode

Total characters295062
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowservices
3rd rowblue-collar
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 8320
25.3%
blue-collar 7407
22.5%
technician 5392
16.4%
services 3177
 
9.6%
management 2341
 
7.1%
retired 1383
 
4.2%
self-employed 1142
 
3.5%
entrepreneur 1142
 
3.5%
housemaid 847
 
2.6%
unemployed 824
 
2.5%
Other values (2) 975
 
3.0%

Length

2024-09-05T21:33:15.724439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 8320
25.3%
blue-collar 7407
22.5%
technician 5392
16.4%
services 3177
 
9.6%
management 2341
 
7.1%
retired 1383
 
4.2%
self-employed 1142
 
3.5%
entrepreneur 1142
 
3.5%
housemaid 847
 
2.6%
unemployed 824
 
2.5%
Other values (2) 975
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e 37806
12.8%
n 28387
 
9.6%
a 26648
 
9.0%
l 25329
 
8.6%
i 24511
 
8.3%
c 21368
 
7.2%
r 16776
 
5.7%
m 15815
 
5.4%
d 13232
 
4.5%
t 11690
 
4.0%
Other values (14) 73500
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278193
94.3%
Dash Punctuation 8549
 
2.9%
Other Punctuation 8320
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 37806
13.6%
n 28387
10.2%
a 26648
9.6%
l 25329
9.1%
i 24511
8.8%
c 21368
 
7.7%
r 16776
 
6.0%
m 15815
 
5.7%
d 13232
 
4.8%
t 11690
 
4.2%
Other values (12) 56631
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 8549
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278193
94.3%
Common 16869
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 37806
13.6%
n 28387
10.2%
a 26648
9.6%
l 25329
9.1%
i 24511
8.8%
c 21368
 
7.7%
r 16776
 
6.0%
m 15815
 
5.7%
d 13232
 
4.8%
t 11690
 
4.2%
Other values (12) 56631
20.4%
Common
ValueCountFrequency (%)
- 8549
50.7%
. 8320
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 37806
12.8%
n 28387
 
9.6%
a 26648
 
9.0%
l 25329
 
8.6%
i 24511
 
8.3%
c 21368
 
7.2%
r 16776
 
5.7%
m 15815
 
5.4%
d 13232
 
4.5%
t 11690
 
4.0%
Other values (14) 73500
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
married
19971 
single
9229 
divorced
3680 
unknown
 
70

Length

Max length8
Median length7
Mean length6.8315933
Min length6

Characters and Unicode

Total characters225101
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowsingle
5th rowdivorced

Common Values

ValueCountFrequency (%)
married 19971
60.6%
single 9229
28.0%
divorced 3680
 
11.2%
unknown 70
 
0.2%

Length

2024-09-05T21:33:16.218610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:16.702018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 19971
60.6%
single 9229
28.0%
divorced 3680
 
11.2%
unknown 70
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 43622
19.4%
i 32880
14.6%
e 32880
14.6%
d 27331
12.1%
m 19971
8.9%
a 19971
8.9%
n 9439
 
4.2%
s 9229
 
4.1%
g 9229
 
4.1%
l 9229
 
4.1%
Other values (6) 11320
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225101
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 43622
19.4%
i 32880
14.6%
e 32880
14.6%
d 27331
12.1%
m 19971
8.9%
a 19971
8.9%
n 9439
 
4.2%
s 9229
 
4.1%
g 9229
 
4.1%
l 9229
 
4.1%
Other values (6) 11320
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 225101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 43622
19.4%
i 32880
14.6%
e 32880
14.6%
d 27331
12.1%
m 19971
8.9%
a 19971
8.9%
n 9439
 
4.2%
s 9229
 
4.1%
g 9229
 
4.1%
l 9229
 
4.1%
Other values (6) 11320
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 43622
19.4%
i 32880
14.6%
e 32880
14.6%
d 27331
12.1%
m 19971
8.9%
a 19971
8.9%
n 9439
 
4.2%
s 9229
 
4.1%
g 9229
 
4.1%
l 9229
 
4.1%
Other values (6) 11320
 
5.0%

schooling
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing9770
Missing (%)29.7%
Memory size1.8 MiB
university.degree
6868 
high.school
5335 
basic.9y
3432 
professional.course
2951 
basic.4y
2330 
Other values (3)
2264 

Length

Max length19
Median length17
Mean length12.717472
Min length7

Characters and Unicode

Total characters294791
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh.school
2nd rowprofessional.course
3rd rowuniversity.degree
4th rowhigh.school
5th rowbasic.4y

Common Values

ValueCountFrequency (%)
university.degree 6868
20.8%
high.school 5335
16.2%
basic.9y 3432
 
10.4%
professional.course 2951
 
9.0%
basic.4y 2330
 
7.1%
basic.6y 1292
 
3.9%
unknown 957
 
2.9%
illiterate 15
 
< 0.1%
(Missing) 9770
29.7%

Length

2024-09-05T21:33:17.276035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:17.764562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 6868
29.6%
high.school 5335
23.0%
basic.9y 3432
14.8%
professional.course 2951
12.7%
basic.4y 2330
 
10.1%
basic.6y 1292
 
5.6%
unknown 957
 
4.1%
illiterate 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 33404
 
11.3%
i 29106
 
9.9%
s 28110
 
9.5%
. 22208
 
7.5%
o 20480
 
6.9%
r 19653
 
6.7%
h 16005
 
5.4%
c 15340
 
5.2%
y 13922
 
4.7%
n 12690
 
4.3%
Other values (15) 83873
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 265529
90.1%
Other Punctuation 22208
 
7.5%
Decimal Number 7054
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33404
12.6%
i 29106
11.0%
s 28110
10.6%
o 20480
 
7.7%
r 19653
 
7.4%
h 16005
 
6.0%
c 15340
 
5.8%
y 13922
 
5.2%
n 12690
 
4.8%
g 12203
 
4.6%
Other values (11) 64616
24.3%
Decimal Number
ValueCountFrequency (%)
9 3432
48.7%
4 2330
33.0%
6 1292
 
18.3%
Other Punctuation
ValueCountFrequency (%)
. 22208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 265529
90.1%
Common 29262
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33404
12.6%
i 29106
11.0%
s 28110
10.6%
o 20480
 
7.7%
r 19653
 
7.4%
h 16005
 
6.0%
c 15340
 
5.8%
y 13922
 
5.2%
n 12690
 
4.8%
g 12203
 
4.6%
Other values (11) 64616
24.3%
Common
ValueCountFrequency (%)
. 22208
75.9%
9 3432
 
11.7%
4 2330
 
8.0%
6 1292
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33404
 
11.3%
i 29106
 
9.9%
s 28110
 
9.5%
. 22208
 
7.5%
o 20480
 
6.9%
r 19653
 
6.7%
h 16005
 
5.4%
c 15340
 
5.2%
y 13922
 
4.7%
n 12690
 
4.3%
Other values (15) 83873
28.5%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
no
25969 
unknown
6979 
yes
 
2

Length

Max length7
Median length2
Mean length3.0590895
Min length2

Characters and Unicode

Total characters100797
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowunknown
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 25969
78.8%
unknown 6979
 
21.2%
yes 2
 
< 0.1%

Length

2024-09-05T21:33:18.275620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:18.675693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 25969
78.8%
unknown 6979
 
21.2%
yes 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 46906
46.5%
o 32948
32.7%
u 6979
 
6.9%
k 6979
 
6.9%
w 6979
 
6.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100797
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 46906
46.5%
o 32948
32.7%
u 6979
 
6.9%
k 6979
 
6.9%
w 6979
 
6.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 100797
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 46906
46.5%
o 32948
32.7%
u 6979
 
6.9%
k 6979
 
6.9%
w 6979
 
6.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 46906
46.5%
o 32948
32.7%
u 6979
 
6.9%
k 6979
 
6.9%
w 6979
 
6.9%
y 2
 
< 0.1%
e 2
 
< 0.1%
s 2
 
< 0.1%

housing
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
yes
17276 
no
14868 
unknown
 
806

Length

Max length7
Median length3
Mean length2.6466161
Min length2

Characters and Unicode

Total characters87206
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes 17276
52.4%
no 14868
45.1%
unknown 806
 
2.4%

Length

2024-09-05T21:33:19.097276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:19.509640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 17276
52.4%
no 14868
45.1%
unknown 806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 17286
19.8%
y 17276
19.8%
e 17276
19.8%
s 17276
19.8%
o 15674
18.0%
u 806
 
0.9%
k 806
 
0.9%
w 806
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87206
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17286
19.8%
y 17276
19.8%
e 17276
19.8%
s 17276
19.8%
o 15674
18.0%
u 806
 
0.9%
k 806
 
0.9%
w 806
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 87206
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 17286
19.8%
y 17276
19.8%
e 17276
19.8%
s 17276
19.8%
o 15674
18.0%
u 806
 
0.9%
k 806
 
0.9%
w 806
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 17286
19.8%
y 17276
19.8%
e 17276
19.8%
s 17276
19.8%
o 15674
18.0%
u 806
 
0.9%
k 806
 
0.9%
w 806
 
0.9%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
no
27175 
yes
4969 
unknown
 
806

Length

Max length7
Median length2
Mean length2.2731108
Min length2

Characters and Unicode

Total characters74899
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 27175
82.5%
yes 4969
 
15.1%
unknown 806
 
2.4%

Length

2024-09-05T21:33:19.959661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:20.308233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 27175
82.5%
yes 4969
 
15.1%
unknown 806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 29593
39.5%
o 27981
37.4%
y 4969
 
6.6%
e 4969
 
6.6%
s 4969
 
6.6%
u 806
 
1.1%
k 806
 
1.1%
w 806
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74899
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 29593
39.5%
o 27981
37.4%
y 4969
 
6.6%
e 4969
 
6.6%
s 4969
 
6.6%
u 806
 
1.1%
k 806
 
1.1%
w 806
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 74899
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 29593
39.5%
o 27981
37.4%
y 4969
 
6.6%
e 4969
 
6.6%
s 4969
 
6.6%
u 806
 
1.1%
k 806
 
1.1%
w 806
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 29593
39.5%
o 27981
37.4%
y 4969
 
6.6%
e 4969
 
6.6%
s 4969
 
6.6%
u 806
 
1.1%
k 806
 
1.1%
w 806
 
1.1%

contact
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
cellular
20901 
telephone
12049 

Length

Max length9
Median length8
Mean length8.3656753
Min length8

Characters and Unicode

Total characters275649
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowcellular
3rd rowcellular
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 20901
63.4%
telephone 12049
36.6%

Length

2024-09-05T21:33:20.683168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:21.054585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 20901
63.4%
telephone 12049
36.6%

Most occurring characters

ValueCountFrequency (%)
l 74752
27.1%
e 57048
20.7%
c 20901
 
7.6%
u 20901
 
7.6%
a 20901
 
7.6%
r 20901
 
7.6%
t 12049
 
4.4%
p 12049
 
4.4%
h 12049
 
4.4%
o 12049
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 275649
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 74752
27.1%
e 57048
20.7%
c 20901
 
7.6%
u 20901
 
7.6%
a 20901
 
7.6%
r 20901
 
7.6%
t 12049
 
4.4%
p 12049
 
4.4%
h 12049
 
4.4%
o 12049
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 275649
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 74752
27.1%
e 57048
20.7%
c 20901
 
7.6%
u 20901
 
7.6%
a 20901
 
7.6%
r 20901
 
7.6%
t 12049
 
4.4%
p 12049
 
4.4%
h 12049
 
4.4%
o 12049
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 74752
27.1%
e 57048
20.7%
c 20901
 
7.6%
u 20901
 
7.6%
a 20901
 
7.6%
r 20901
 
7.6%
t 12049
 
4.4%
p 12049
 
4.4%
h 12049
 
4.4%
o 12049
 
4.4%

month
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
may
10955 
jul
5822 
aug
4937 
jun
4263 
nov
3288 
Other values (5)
3685 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98850
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsep
2nd rowsep
3rd rowmay
4th rowaug
5th rownov

Common Values

ValueCountFrequency (%)
may 10955
33.2%
jul 5822
17.7%
aug 4937
15.0%
jun 4263
 
12.9%
nov 3288
 
10.0%
apr 2081
 
6.3%
oct 562
 
1.7%
sep 449
 
1.4%
mar 440
 
1.3%
dec 153
 
0.5%

Length

2024-09-05T21:33:21.442465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:21.886620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 10955
33.2%
jul 5822
17.7%
aug 4937
15.0%
jun 4263
 
12.9%
nov 3288
 
10.0%
apr 2081
 
6.3%
oct 562
 
1.7%
sep 449
 
1.4%
mar 440
 
1.3%
dec 153
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 18413
18.6%
u 15022
15.2%
m 11395
11.5%
y 10955
11.1%
j 10085
10.2%
n 7551
7.6%
l 5822
 
5.9%
g 4937
 
5.0%
o 3850
 
3.9%
v 3288
 
3.3%
Other values (7) 7532
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98850
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18413
18.6%
u 15022
15.2%
m 11395
11.5%
y 10955
11.1%
j 10085
10.2%
n 7551
7.6%
l 5822
 
5.9%
g 4937
 
5.0%
o 3850
 
3.9%
v 3288
 
3.3%
Other values (7) 7532
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 98850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18413
18.6%
u 15022
15.2%
m 11395
11.5%
y 10955
11.1%
j 10085
10.2%
n 7551
7.6%
l 5822
 
5.9%
g 4937
 
5.0%
o 3850
 
3.9%
v 3288
 
3.3%
Other values (7) 7532
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18413
18.6%
u 15022
15.2%
m 11395
11.5%
y 10955
11.1%
j 10085
10.2%
n 7551
7.6%
l 5822
 
5.9%
g 4937
 
5.0%
o 3850
 
3.9%
v 3288
 
3.3%
Other values (7) 7532
7.6%

day_of_week
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3328
Missing (%)10.1%
Memory size1.8 MiB
thu
6196 
mon
6038 
wed
5883 
tue
5809 
fri
5696 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters88866
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwed
2nd rowtue
3rd rowthu
4th rowwed
5th rowtue

Common Values

ValueCountFrequency (%)
thu 6196
18.8%
mon 6038
18.3%
wed 5883
17.9%
tue 5809
17.6%
fri 5696
17.3%
(Missing) 3328
10.1%

Length

2024-09-05T21:33:22.442194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:22.794233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 6196
20.9%
mon 6038
20.4%
wed 5883
19.9%
tue 5809
19.6%
fri 5696
19.2%

Most occurring characters

ValueCountFrequency (%)
t 12005
13.5%
u 12005
13.5%
e 11692
13.2%
h 6196
7.0%
m 6038
6.8%
o 6038
6.8%
n 6038
6.8%
w 5883
6.6%
d 5883
6.6%
f 5696
6.4%
Other values (2) 11392
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88866
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12005
13.5%
u 12005
13.5%
e 11692
13.2%
h 6196
7.0%
m 6038
6.8%
o 6038
6.8%
n 6038
6.8%
w 5883
6.6%
d 5883
6.6%
f 5696
6.4%
Other values (2) 11392
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 88866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12005
13.5%
u 12005
13.5%
e 11692
13.2%
h 6196
7.0%
m 6038
6.8%
o 6038
6.8%
n 6038
6.8%
w 5883
6.6%
d 5883
6.6%
f 5696
6.4%
Other values (2) 11392
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 12005
13.5%
u 12005
13.5%
e 11692
13.2%
h 6196
7.0%
m 6038
6.8%
o 6038
6.8%
n 6038
6.8%
w 5883
6.6%
d 5883
6.6%
f 5696
6.4%
Other values (2) 11392
12.8%

campaign
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5765706
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:23.204736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.784839
Coefficient of variation (CV)1.0808316
Kurtosis36.8918
Mean2.5765706
Median Absolute Deviation (MAD)1
Skewness4.7473925
Sum84898
Variance7.7553283
MonotonicityNot monotonic
2024-09-05T21:33:23.993743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 14100
42.8%
2 8411
25.5%
3 4296
 
13.0%
4 2109
 
6.4%
5 1314
 
4.0%
6 774
 
2.3%
7 516
 
1.6%
8 327
 
1.0%
9 213
 
0.6%
10 185
 
0.6%
Other values (31) 705
 
2.1%
ValueCountFrequency (%)
1 14100
42.8%
2 8411
25.5%
3 4296
 
13.0%
4 2109
 
6.4%
5 1314
 
4.0%
6 774
 
2.3%
7 516
 
1.6%
8 327
 
1.0%
9 213
 
0.6%
10 185
 
0.6%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
< 0.1%
42 2
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
37 1
 
< 0.1%
35 4
< 0.1%
34 2
< 0.1%
33 2
< 0.1%
32 4
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.86519
Minimum0
Maximum999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:24.419608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.95368
Coefficient of variation (CV)0.19312535
Kurtosis22.524221
Mean962.86519
Median Absolute Deviation (MAD)0
Skewness-4.9520123
Sum31726408
Variance34578.77
MonotonicityNot monotonic
2024-09-05T21:33:24.832708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
999 31751
96.4%
3 353
 
1.1%
6 322
 
1.0%
4 93
 
0.3%
2 48
 
0.1%
9 47
 
0.1%
12 46
 
0.1%
7 44
 
0.1%
10 44
 
0.1%
5 39
 
0.1%
Other values (16) 163
 
0.5%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 22
 
0.1%
2 48
 
0.1%
3 353
1.1%
4 93
 
0.3%
5 39
 
0.1%
6 322
1.0%
7 44
 
0.1%
8 15
 
< 0.1%
9 47
 
0.1%
ValueCountFrequency (%)
999 31751
96.4%
27 1
 
< 0.1%
26 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
< 0.1%
18 7
 
< 0.1%
17 6
 
< 0.1%
16 7
 
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17044006
Minimum0
Maximum7
Zeros28503
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:25.192529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48993043
Coefficient of variation (CV)2.8745028
Kurtosis20.026123
Mean0.17044006
Median Absolute Deviation (MAD)0
Skewness3.8277984
Sum5616
Variance0.24003183
MonotonicityNot monotonic
2024-09-05T21:33:25.556210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 28503
86.5%
1 3614
 
11.0%
2 589
 
1.8%
3 173
 
0.5%
4 56
 
0.2%
5 10
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 28503
86.5%
1 3614
 
11.0%
2 589
 
1.8%
3 173
 
0.5%
4 56
 
0.2%
5 10
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 4
 
< 0.1%
5 10
 
< 0.1%
4 56
 
0.2%
3 173
 
0.5%
2 589
 
1.8%
1 3614
 
11.0%
0 28503
86.5%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
nonexistent
28503 
failure
3357 
success
 
1090

Length

Max length11
Median length11
Mean length10.460152
Min length7

Characters and Unicode

Total characters344662
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfailure
2nd rowsuccess
3rd rowfailure
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 28503
86.5%
failure 3357
 
10.2%
success 1090
 
3.3%

Length

2024-09-05T21:33:26.014302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:33:26.394383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 28503
86.5%
failure 3357
 
10.2%
success 1090
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 85509
24.8%
e 61453
17.8%
t 57006
16.5%
i 31860
 
9.2%
s 31773
 
9.2%
o 28503
 
8.3%
x 28503
 
8.3%
u 4447
 
1.3%
f 3357
 
1.0%
a 3357
 
1.0%
Other values (3) 8894
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 344662
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 85509
24.8%
e 61453
17.8%
t 57006
16.5%
i 31860
 
9.2%
s 31773
 
9.2%
o 28503
 
8.3%
x 28503
 
8.3%
u 4447
 
1.3%
f 3357
 
1.0%
a 3357
 
1.0%
Other values (3) 8894
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 344662
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 85509
24.8%
e 61453
17.8%
t 57006
16.5%
i 31860
 
9.2%
s 31773
 
9.2%
o 28503
 
8.3%
x 28503
 
8.3%
u 4447
 
1.3%
f 3357
 
1.0%
a 3357
 
1.0%
Other values (3) 8894
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 85509
24.8%
e 61453
17.8%
t 57006
16.5%
i 31860
 
9.2%
s 31773
 
9.2%
o 28503
 
8.3%
x 28503
 
8.3%
u 4447
 
1.3%
f 3357
 
1.0%
a 3357
 
1.0%
Other values (3) 8894
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.088257967
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative13668
Negative (%)41.5%
Memory size257.6 KiB
2024-09-05T21:33:26.693804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5720194
Coefficient of variation (CV)17.811643
Kurtosis-1.043921
Mean0.088257967
Median Absolute Deviation (MAD)0.3
Skewness-0.73639982
Sum2908.1
Variance2.4712449
MonotonicityNot monotonic
2024-09-05T21:33:27.034693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 13068
39.7%
-1.8 7262
22.0%
1.1 6214
18.9%
-0.1 2944
 
8.9%
-2.9 1345
 
4.1%
-3.4 868
 
2.6%
-1.7 609
 
1.8%
-1.1 487
 
1.5%
-3 144
 
0.4%
-0.2 9
 
< 0.1%
ValueCountFrequency (%)
-3.4 868
 
2.6%
-3 144
 
0.4%
-2.9 1345
 
4.1%
-1.8 7262
22.0%
-1.7 609
 
1.8%
-1.1 487
 
1.5%
-0.2 9
 
< 0.1%
-0.1 2944
 
8.9%
1.1 6214
18.9%
1.4 13068
39.7%
ValueCountFrequency (%)
1.4 13068
39.7%
1.1 6214
18.9%
-0.1 2944
 
8.9%
-0.2 9
 
< 0.1%
-1.1 487
 
1.5%
-1.7 609
 
1.8%
-1.8 7262
22.0%
-2.9 1345
 
4.1%
-3 144
 
0.4%
-3.4 868
 
2.6%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.576836
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:27.387556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.798
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57885731
Coefficient of variation (CV)0.0061859038
Kurtosis-0.81977742
Mean93.576836
Median Absolute Deviation (MAD)0.354
Skewness-0.24226999
Sum3083356.8
Variance0.33507578
MonotonicityNot monotonic
2024-09-05T21:33:27.797549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 6214
18.9%
93.918 5437
16.5%
92.893 4566
13.9%
93.444 4124
12.5%
94.465 3507
10.6%
93.2 2896
8.8%
93.075 1943
 
5.9%
92.201 628
 
1.9%
92.963 572
 
1.7%
92.431 352
 
1.1%
Other values (16) 2711
8.2%
ValueCountFrequency (%)
92.201 628
 
1.9%
92.379 219
 
0.7%
92.431 352
 
1.1%
92.469 145
 
0.4%
92.649 297
 
0.9%
92.713 144
 
0.4%
92.756 9
 
< 0.1%
92.843 225
 
0.7%
92.893 4566
13.9%
92.963 572
 
1.7%
ValueCountFrequency (%)
94.767 95
 
0.3%
94.601 162
 
0.5%
94.465 3507
10.6%
94.215 240
 
0.7%
94.199 230
 
0.7%
94.055 184
 
0.6%
94.027 185
 
0.6%
93.994 6214
18.9%
93.918 5437
16.5%
93.876 175
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.483772
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative32950
Negative (%)100.0%
Memory size257.6 KiB
2024-09-05T21:33:28.176746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6225847
Coefficient of variation (CV)-0.11418364
Kurtosis-0.35684683
Mean-40.483772
Median Absolute Deviation (MAD)4.4
Skewness0.30584544
Sum-1333940.3
Variance21.368289
MonotonicityNot monotonic
2024-09-05T21:33:28.572201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 6214
18.9%
-42.7 5437
16.5%
-46.2 4566
13.9%
-36.1 4124
12.5%
-41.8 3507
10.6%
-42 2896
8.8%
-47.1 1943
 
5.9%
-31.4 628
 
1.9%
-40.8 572
 
1.7%
-26.9 352
 
1.1%
Other values (16) 2711
8.2%
ValueCountFrequency (%)
-50.8 95
 
0.3%
-50 225
 
0.7%
-49.5 162
 
0.5%
-47.1 1943
 
5.9%
-46.2 4566
13.9%
-45.9 9
 
< 0.1%
-42.7 5437
16.5%
-42 2896
8.8%
-41.8 3507
10.6%
-40.8 572
 
1.7%
ValueCountFrequency (%)
-26.9 352
 
1.1%
-29.8 219
 
0.7%
-30.1 297
 
0.9%
-31.4 628
 
1.9%
-33 144
 
0.4%
-33.6 145
 
0.4%
-34.6 138
 
0.4%
-34.8 215
 
0.7%
-36.1 4124
12.5%
-36.4 6214
18.9%

euribor3m
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6298817
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:29.013195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.79
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.732277
Coefficient of variation (CV)0.47722685
Kurtosis-1.3889605
Mean3.6298817
Median Absolute Deviation (MAD)0.107
Skewness-0.72113517
Sum119604.6
Variance3.0007836
MonotonicityNot monotonic
2024-09-05T21:33:29.485986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2265
 
6.9%
4.962 2127
 
6.5%
4.963 1999
 
6.1%
4.961 1525
 
4.6%
4.856 988
 
3.0%
1.405 945
 
2.9%
4.964 931
 
2.8%
4.965 878
 
2.7%
4.864 834
 
2.5%
4.96 805
 
2.4%
Other values (302) 19653
59.6%
ValueCountFrequency (%)
0.634 6
 
< 0.1%
0.635 35
0.1%
0.636 9
 
< 0.1%
0.637 3
 
< 0.1%
0.638 4
 
< 0.1%
0.639 12
 
< 0.1%
0.64 7
 
< 0.1%
0.642 25
0.1%
0.643 20
0.1%
0.644 31
0.1%
ValueCountFrequency (%)
5.045 5
 
< 0.1%
5 5
 
< 0.1%
4.97 127
 
0.4%
4.968 793
 
2.4%
4.967 512
 
1.6%
4.966 490
 
1.5%
4.965 878
2.7%
4.964 931
2.8%
4.963 1999
6.1%
4.962 2127
6.5%

nr.employed
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.4009
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:29.850938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.128548
Coefficient of variation (CV)0.01395838
Kurtosis0.0027408101
Mean5167.4009
Median Absolute Deviation (MAD)37.1
Skewness-1.0505836
Sum1.7026586 × 108
Variance5202.5274
MonotonicityNot monotonic
2024-09-05T21:33:30.225574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 13068
39.7%
5099.1 6734
20.4%
5191 6214
18.9%
5195.8 2944
 
8.9%
5076.2 1345
 
4.1%
5017.5 868
 
2.6%
4991.6 609
 
1.8%
5008.7 528
 
1.6%
4963.6 487
 
1.5%
5023.5 144
 
0.4%
ValueCountFrequency (%)
4963.6 487
 
1.5%
4991.6 609
 
1.8%
5008.7 528
 
1.6%
5017.5 868
 
2.6%
5023.5 144
 
0.4%
5076.2 1345
 
4.1%
5099.1 6734
20.4%
5176.3 9
 
< 0.1%
5191 6214
18.9%
5195.8 2944
8.9%
ValueCountFrequency (%)
5228.1 13068
39.7%
5195.8 2944
 
8.9%
5191 6214
18.9%
5176.3 9
 
< 0.1%
5099.1 6734
20.4%
5076.2 1345
 
4.1%
5023.5 144
 
0.4%
5017.5 868
 
2.6%
5008.7 528
 
1.6%
4991.6 609
 
1.8%

pmonths
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.65516
Minimum0
Maximum999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:30.604635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation187.03308
Coefficient of variation (CV)0.19428876
Kurtosis22.522598
Mean962.65516
Median Absolute Deviation (MAD)0
Skewness-4.9518916
Sum31719488
Variance34981.373
MonotonicityNot monotonic
2024-09-05T21:33:31.031220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
999 31751
96.4%
0.1 353
 
1.1%
0.2 322
 
1.0%
0.1333333333 93
 
0.3%
0.06666666667 48
 
0.1%
0.3 47
 
0.1%
0.4 46
 
0.1%
0.2333333333 44
 
0.1%
0.3333333333 44
 
0.1%
0.1666666667 39
 
0.1%
Other values (16) 163
 
0.5%
ValueCountFrequency (%)
0 12
 
< 0.1%
0.03333333333 22
 
0.1%
0.06666666667 48
 
0.1%
0.1 353
1.1%
0.1333333333 93
 
0.3%
0.1666666667 39
 
0.1%
0.2 322
1.0%
0.2333333333 44
 
0.1%
0.2666666667 15
 
< 0.1%
0.3 47
 
0.1%
ValueCountFrequency (%)
999 31751
96.4%
0.9 1
 
< 0.1%
0.8666666667 1
 
< 0.1%
0.7333333333 2
 
< 0.1%
0.7 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.6333333333 2
 
< 0.1%
0.6 7
 
< 0.1%
0.5666666667 6
 
< 0.1%
0.5333333333 7
 
< 0.1%

pastEmail
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34172989
Minimum0
Maximum28
Zeros29120
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:31.393766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2460764
Coefficient of variation (CV)3.646378
Kurtosis64.958038
Mean0.34172989
Median Absolute Deviation (MAD)0
Skewness6.3888733
Sum11260
Variance1.5527063
MonotonicityNot monotonic
2024-09-05T21:33:31.757291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 29120
88.4%
2 1107
 
3.4%
1 979
 
3.0%
3 735
 
2.2%
4 490
 
1.5%
6 197
 
0.6%
5 121
 
0.4%
8 65
 
0.2%
12 37
 
0.1%
9 32
 
0.1%
Other values (11) 67
 
0.2%
ValueCountFrequency (%)
0 29120
88.4%
1 979
 
3.0%
2 1107
 
3.4%
3 735
 
2.2%
4 490
 
1.5%
5 121
 
0.4%
6 197
 
0.6%
7 14
 
< 0.1%
8 65
 
0.2%
9 32
 
0.1%
ValueCountFrequency (%)
28 1
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
21 2
 
< 0.1%
20 3
 
< 0.1%
18 6
 
< 0.1%
16 5
 
< 0.1%
15 8
 
< 0.1%
14 1
 
< 0.1%
12 37
0.1%

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct32950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16475.5
Minimum1
Maximum32950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-09-05T21:33:32.215441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1648.45
Q18238.25
median16475.5
Q324712.75
95-th percentile31302.55
Maximum32950
Range32949
Interquartile range (IQR)16474.5

Descriptive statistics

Standard deviation9511.99
Coefficient of variation (CV)0.57734151
Kurtosis-1.2
Mean16475.5
Median Absolute Deviation (MAD)8237.5
Skewness0
Sum5.4286772 × 108
Variance90477954
MonotonicityStrictly increasing
2024-09-05T21:33:32.671788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
22010 1
 
< 0.1%
21976 1
 
< 0.1%
21975 1
 
< 0.1%
21974 1
 
< 0.1%
21973 1
 
< 0.1%
21972 1
 
< 0.1%
21971 1
 
< 0.1%
21970 1
 
< 0.1%
21969 1
 
< 0.1%
Other values (32940) 32940
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
32950 1
< 0.1%
32949 1
< 0.1%
32948 1
< 0.1%
32947 1
< 0.1%
32946 1
< 0.1%
32945 1
< 0.1%
32944 1
< 0.1%
32943 1
< 0.1%
32942 1
< 0.1%
32941 1
< 0.1%

Interactions

2024-09-05T21:33:07.668624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:22.780367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:27.297421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:31.739517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:35.716253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:39.738309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:43.617446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:47.601797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:51.660213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:55.529313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:59.794900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:03.730078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:08.055758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:23.155129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:27.720774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:32.105149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:36.087603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:40.085193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:43.985048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:47.967930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:52.025104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:55.905640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:00.173219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:04.098779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:08.389115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:23.504647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:28.064244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:32.441881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:36.422550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:40.417974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:44.320085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:48.292872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:52.338720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:56.236714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:00.495918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:04.402593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:08.722907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:23.846663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:28.408772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:32.767623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:36.751497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:40.758506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:44.649762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:48.632592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:52.633003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:56.549762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:00.812029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:04.742221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:09.072999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:24.180417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:28.755286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:33.116052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:37.089701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:41.099890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:44.992072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:48.977853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:53.002358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:56.903647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:01.162073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:05.098876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:09.406073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:24.530554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:29.082968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:33.448423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:37.407171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:41.404840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:45.312350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:49.294694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:53.305624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:57.216972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:01.479802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:05.402259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:09.723422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:24.863602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:29.409655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:33.757220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:37.742098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:41.737055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:45.654633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:49.625696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:53.617033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:57.523576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:01.807413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:05.744816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:10.047561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:25.206460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:29.731353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:34.082459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:38.075686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:42.052247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:45.976064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:49.935141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:53.919588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:58.209244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:02.117838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:06.063783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:10.381510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:25.569662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:30.048623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:34.405295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:38.383794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:42.359812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:46.285340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:50.250238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:54.230574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:58.511333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:02.445349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:06.363502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:10.732167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:25.966046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:30.372589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:34.735651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:38.724423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:42.667272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:46.617726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:50.582805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:54.557396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:58.828240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:02.754887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:06.690632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:11.133544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:26.387881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:31.092894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:35.063946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:39.062738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:42.987876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:46.929445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:50.989305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:54.869899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:59.137377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:03.079990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:07.014091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:11.473478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:26.822123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:31.414812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:35.384199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:39.389777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:43.300295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:47.258296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:51.327388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:55.197133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:32:59.454615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:03.397075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:33:07.338756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-09-05T21:33:33.064631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
campaigncons.conf.idxcons.price.idxcontactcustAgeday_of_weekdefaultemp.var.rateeuribor3mhousingidloanmaritalmonthnr.employedpastEmailpdayspmonthspoutcomepreviousprofessionschooling
campaign1.0000.0010.0950.0610.0060.0160.0180.1580.1440.0240.0090.0240.0050.0470.146-0.0830.0600.0600.048-0.0910.0000.000
cons.conf.idx0.0011.0000.2400.4170.1140.0440.1400.2140.2280.042-0.0060.0090.0760.6000.123-0.105-0.079-0.0790.369-0.1110.1110.064
cons.price.idx0.0950.2401.0000.6740.0410.0500.1560.6640.4910.0700.0030.0200.0700.6760.466-0.2630.0580.0580.384-0.2850.1330.098
contact0.0610.4170.6741.0000.0320.0550.1380.2290.1380.0870.0030.0290.0770.6110.107-0.2220.1180.1180.243-0.2420.1280.122
custAge0.0060.1140.0410.0321.0000.0260.1430.0430.0520.0080.0020.0070.2580.0970.045-0.012-0.000-0.0000.115-0.0140.2510.117
day_of_week0.0160.0440.0500.0550.0261.0000.0130.0330.0330.012-0.0070.0060.0140.0670.030-0.010-0.014-0.0140.018-0.0100.0170.023
default0.0180.1400.1560.1380.1430.0131.0000.1800.1720.0090.0050.0000.0960.1140.160-0.0970.0810.0810.077-0.1060.1560.180
emp.var.rate0.1580.2140.6640.2290.0430.0330.1801.0000.9400.0530.0080.0100.0710.6590.945-0.4000.2270.2270.377-0.4350.1370.067
euribor3m0.1440.2280.4910.1380.0520.0330.1720.9401.0000.0550.0100.0110.0700.5520.929-0.4150.2770.2770.416-0.4530.1300.063
housing0.0240.0420.0700.0870.0080.0120.0090.0530.0551.0000.0050.7080.0090.056-0.0370.025-0.013-0.0130.0190.0270.0160.010
id0.009-0.0060.0030.0030.002-0.0070.0050.0080.0100.0051.0000.0050.0000.0050.006-0.0030.0150.0150.009-0.0040.0110.005
loan0.0240.0090.0200.0290.0070.0060.0000.0100.0110.7080.0051.0000.0000.0210.0020.0040.0030.0030.0040.0020.0130.000
marital0.0050.0760.0700.0770.2580.0140.0960.0710.0700.0090.0000.0001.0000.052-0.0750.041-0.040-0.0400.0390.0440.1840.114
month0.0470.6000.6760.6110.0970.0670.1140.6590.5520.0560.0050.0210.0521.000-0.3650.111-0.045-0.0450.2410.1240.1110.095
nr.employed0.1460.1230.4660.1070.0450.0300.1600.9450.929-0.0370.0060.002-0.075-0.3651.000-0.4030.2890.2890.412-0.4380.1360.068
pastEmail-0.083-0.105-0.263-0.222-0.012-0.010-0.097-0.400-0.4150.025-0.0030.0040.0410.111-0.4031.000-0.460-0.4600.4390.9160.0390.013
pdays0.060-0.0790.0580.118-0.000-0.0140.0810.2270.277-0.0130.0150.003-0.040-0.0450.289-0.4601.0001.0000.953-0.5100.1410.058
pmonths0.060-0.0790.0580.118-0.000-0.0140.0810.2270.277-0.0130.0150.003-0.040-0.0450.289-0.4601.0001.0000.953-0.5100.1410.058
poutcome0.0480.3690.3840.2430.1150.0180.0770.3770.4160.0190.0090.0040.0390.2410.4120.4390.9530.9531.000-0.4950.0990.043
previous-0.091-0.111-0.285-0.242-0.014-0.010-0.106-0.435-0.4530.027-0.0040.0020.0440.124-0.4380.916-0.510-0.510-0.4951.0000.0550.019
profession0.0000.1110.1330.1280.2510.0170.1560.1370.1300.0160.0110.0130.1840.1110.1360.0390.1410.1410.0990.0551.0000.360
schooling0.0000.0640.0980.1220.1170.0230.1800.0670.0630.0100.0050.0000.1140.0950.0680.0130.0580.0580.0430.0190.3601.000

Missing values

2024-09-05T21:33:12.065668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-05T21:33:13.118124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-05T21:33:13.889001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailid
0NaNadmin.marriedNaNnonoyescellularsepwed29991failure-1.194.199-37.50.8864963.6999.021
135.0servicesmarriedhigh.schoolnononocellularseptue231success-3.492.379-29.80.7885017.50.122
250.0blue-collarmarriedprofessional.courseunknownyesnocellularmaythu19991failure-1.892.893-46.21.3275099.1999.023
330.0admin.singleuniversity.degreenononocellularaugwed19990nonexistent1.493.444-36.14.9645228.1999.004
439.0servicesdivorcedhigh.schoolnoyesnocellularnovtue19990nonexistent-0.193.200-42.04.1535195.8999.005
5NaNstudentsingleNaNnoyesnocellularjulthu19990nonexistent1.493.918-42.74.9585228.1999.006
636.0housemaidmarriedbasic.4ynoyesnocellularaprNaN79990nonexistent-1.893.075-47.11.4155099.1999.007
730.0admin.singlebasic.9ynoyesnotelephonejulthu19990nonexistent1.493.918-42.74.9635228.1999.008
8NaNblue-collarsingleNaNnoyesnotelephonemaywed109990nonexistent1.193.994-36.44.8595191.0999.009
9NaNself-employedmarriedNaNnononocellularaugfri79990nonexistent1.493.444-36.14.9665228.1999.0010
custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailid
3294032.0blue-collarmarriedNaNnoyesnotelephonejunNaN19991failure-1.794.055-39.80.7614991.6999.0432941
32941NaNtechnicianmarriedprofessional.courseunknownnonotelephonemaytue19990nonexistent1.193.994-36.44.8565191.0999.0032942
3294238.0blue-collarmarriedbasic.9yunknownunknownunknowntelephonejunfri19990nonexistent1.494.465-41.84.9675228.1999.0032943
3294336.0self-employedsingleuniversity.degreenonoyescellularaprtue29990nonexistent-1.893.075-47.11.4535099.1999.0032944
3294432.0admin.singleuniversity.degreenoyesnocellularaugtue29990nonexistent1.493.444-36.14.9665228.1999.0032945
32945NaNtechnicianmarriedprofessional.coursenoyesnotelephonemayNaN19990nonexistent1.193.994-36.44.8575191.0999.0032946
3294632.0blue-collarmarriedNaNnonoyescellularmaythu49990nonexistent-1.892.893-46.21.2665099.1999.0032947
3294732.0servicesmarriedhigh.schoolnononocellularmaymon29990nonexistent-1.892.893-46.21.2995099.1999.0032948
3294832.0blue-collarmarriedbasic.9ynoyesnotelephonejunwed19990nonexistent1.494.465-41.84.9595228.1999.0032949
3294925.0admin.singleuniversity.degreenononotelephonejunmon29990nonexistent-2.992.963-40.81.2605076.2999.0032950